Abstract
This paper describes the LiveTweet application, a system for automatically analysing and predicting the interestingness of microblog posts. Based on a stream of recent microblog posts the system tracks user interactions on Twitter that indicate interesting content. An incremental Naive Bayes model is trained to learn the characteristics of tweets which are considered interesting by the users. Finally, the probability of a microblog post to be retweeted is used as metric for its interestingness.
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References
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© 2012 Springer-Verlag Berlin Heidelberg
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Alhadi, A.C., Gottron, T., Kunegis, J., Naveed, N. (2012). LiveTweet: Monitoring and Predicting Interesting Microblog Posts. In: Baeza-Yates, R., et al. Advances in Information Retrieval. ECIR 2012. Lecture Notes in Computer Science, vol 7224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28997-2_66
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DOI: https://doi.org/10.1007/978-3-642-28997-2_66
Publisher Name: Springer, Berlin, Heidelberg
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